The Impact of Visual Cues, Reward, and Motor Feedback on the Representation of Behaviorally Relevant Spatial Locations in Primary Visual Cortex

Sex-specific resilience of neocortex to food restriction

Mammals have evolved sex-specific adaptations to reduce energy usage in times of food scarcity. These adaptations are well described for peripheral tissue, though much less is known about how the energy-expensive brain adapts to food restriction, and how such adaptations differ across the sexes. Here, we examined how food restriction impacts energy usage and function in the primary visual cortex (V1) of adult male and female mice. Molecular analysis and RNA sequencing in V1 revealed that in males, but not in females, food restriction significantly modulated canonical, energy-regulating pathways, including pathways associated waith AMP-activated protein kinase, peroxisome proliferator-activated receptor alpha, mammalian target of rapamycin, and oxidative phosphorylation. Moreover, we found that in contrast to males, food restriction in females did not significantly affect V1 ATP usage or visual coding precision (assessed by orientation selectivity). Decreased serum leptin is known to be necessary for triggering energy-saving changes in V1 during food restriction. Consistent with this, we found significantly decreased serum leptin in food-restricted males but no significant change in food-restricted females. Collectively, our findings demonstrate that cortical function and energy usage in female mice are more resilient to food restriction than in males. The neocortex, therefore, contributes to sex-specific, energy-saving adaptations in response to food restriction.

Natural visual behavior and active sensing in the mouse

In the natural world, animals use vision for a wide variety of behaviors not reflected in most laboratory paradigms. Although mice have low-acuity vision, they use their vision for many natural behaviors, including predator avoidance, prey capture, and navigation. They also perform active sensing, moving their head and eyes to achieve behavioral goals and acquire visual information. These aspects of natural vision result in visual inputs and corresponding behavioral outputs that are outside the range of conventional vision studies but are essential aspects of visual function. Here, we review recent studies in mice that have tapped into natural behavior and active sensing to reveal the computational logic of neural circuits for vision.

Behavior-relevant top-down cross-modal predictions in mouse neocortex

Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone-texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.

Multiple long-range projections convey position information to the agranular retrosplenial cortex

Neuronal signals encoding the animal's position widely modulate neocortical processing. While these signals are assumed to depend on hippocampal output, their origin has not been investigated directly. Here, we asked which brain region sends position information to the retrosplenial cortex (RSC), a key circuit for memory and navigation. We comprehensively characterized the long-range inputs to agranular RSC using two-photon axonal imaging in head-fixed mice performing a spatial task in darkness. Surprisingly, most long-range pathways convey position information, but with notable differences. Axons from the secondary motor and posterior parietal cortex transmit the most position information. By contrast, axons from the anterior cingulate and orbitofrontal cortex and thalamus convey substantially less position information. Axons from the primary and secondary visual cortex contribute negligibly. This demonstrates that the hippocampus is not the only source of position information. Instead, the RSC is a hub in a distributed brain network that shares position information.

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

Interactions between rodent visual and spatial systems during navigation

Many behaviours that are critical for animals to survive and thrive rely on spatial navigation. Spatial navigation, in turn, relies on internal representations about one's spatial location, one's orientation or heading direction and the distance to objects in the environment. Although the importance of vision in guiding such internal representations has long been recognized, emerging evidence suggests that spatial signals can also modulate neural responses in the central visual pathway. Here, we review the bidirectional influences between visual and navigational signals in the rodent brain. Specifically, we discuss reciprocal interactions between vision and the internal representations of spatial position, explore the effects of vision on representations of an animal's heading direction and vice versa, and examine how the visual and navigational systems work together to assess the relative distances of objects and other features. Throughout, we consider how technological advances and novel ethological paradigms that probe rodent visuo-spatial behaviours allow us to advance our understanding of how brain areas of the central visual pathway and the spatial systems interact and enable complex behaviours.

Optogenetic manipulation of inhibitory interneurons can be used to validate a model of spatiotemporal sequence learning

The brain uses temporal information to link discrete events into memory structures supporting recognition, prediction, and a wide variety of complex behaviors. It is still an open question how experience-dependent synaptic plasticity creates memories including temporal and ordinal information. Various models have been proposed to explain how this could work, but these are often difficult to validate in a living brain. A recent model developed to explain sequence learning in the visual cortex encodes intervals in recurrent excitatory synapses and uses a learned offset between excitation and inhibition to generate precisely timed "messenger" cells that signal the end of an instance of time. This mechanism suggests that the recall of stored temporal intervals should be particularly sensitive to the activity of inhibitory interneurons that can be easily targeted in vivo with standard optogenetic tools. In this work we examined how simulated optogenetic manipulations of inhibitory cells modifies temporal learning and recall based on these mechanisms. We show that disinhibition and excess inhibition during learning or testing cause characteristic errors in recalled timing that could be used to validate the model in vivo using either physiological or behavioral measurements.

Pattern Completion and Rate Remapping in Retrosplenial Cortex

Principles governing the encoding, storage, and updating of memories in cortical networks are poorly understood. In retrosplenial cortex (RSC), cells respond to the animal's position as it navigates a real or virtual (VR) linear track. Position correlated cells (PCCs) in RSC require an intact hippocampus to form. To examine whether PCCs undergo pattern completion and remapping like hippocampal cells, neuronal activity in RSC or CA1 was recorded using two-photon calcium imaging in mice running on VR tracks. RSC and CA1 PCC activity underwent global and rate remapping depending on the degree of change to familiar environments. The formation of position correlated fields in both regions required stability across laps; however, once formed, PCCs became robust to object destabilization, indicating pattern completion of the previously formed memory. Thus, memory and remapping properties were conserved between RSC and CA1, suggesting that these functional properties are transmitted to cortex to support memory functions.

Locomotion-induced gain of visual responses cannot explain visuomotor mismatch responses in layer 2/3 of primary visual cortex

The aim of this work is to provide a comment on a recent paper by Muzzu and Saleem (2021), which claims that visuomotor mismatch responses in mouse visual cortex can be explained by a locomotion-induced gain of visual halt responses. Our primary concern is that without directly comparing these responses with mismatch responses, the claim that one response can explain the other appears difficult to uphold, more so because previous work finds that a uniform locomotion-induced gain cannot explain mismatch responses. To support these arguments, we analyze layer 2/3 calcium imaging datasets and show that coupling between visual flow and locomotion greatly enhances mismatch responses in an experience-dependent manner compared with halts in non-coupled visual flow. This is consistent with mismatch responses representing visuomotor prediction errors. Thus, we conclude that while feature selectivity might contribute to mismatch responses in mouse visual cortex, it cannot explain these responses.

Engaging distributed cortical and cerebellar networks through motor execution, observation, and imagery

When we interact with the environment around us, we are sometimes active participants, making directed physical motor movements and other times only mentally engaging with our environment, taking in sensory information and internally planning our next move without directed physical movement. Traditionally, cortical motor regions and key subcortical structures such as the cerebellum have been tightly linked to motor initiation, coordination, and directed motor behavior. However, recent neuroimaging studies have noted the activation of the cerebellum and wider cortical networks specifically during various forms of motor processing, including the observations of actions and mental rehearsal of movements through motor imagery. This phenomenon of cognitive engagement of traditional motor networks raises the question of how these brain regions are involved in the initiation of movement without physical motor output. Here, we will review evidence for distributed brain network activation during motor execution, observation, and imagery in human neuroimaging studies as well as the potential for cerebellar involvement specifically in motor-related cognition. Converging evidence suggests that a common global brain network is involved in both movement execution and motor observation or imagery, with specific task-dependent shifts in these global activation patterns. We will further discuss underlying cross-species anatomical support for these cognitive motor-related functions as well as the role of cerebrocerebellar communication during action observation and motor imagery.

Feasibility and positive effects of scalp acupuncture for modulating motor and cerebral activity in Parkinson's disease: A pilot study

BACKGROUND

A variety of acupuncture therapies have shown efficacy in Parkinson's disease (PD).

OBJECTIVE

To evaluate scalp acupuncture (SA) effects on motor and cerebral activity by using gait equipment and resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Twelve patients with PD received SA. They underwent the first functional-imaging scan after tactile stimulation and the second scan following needle removal. Gait test and local sensation assessment were performed immediately after each functional scan. Gait parameter differences between pre- and post-SA were analyzed using a paired t-test and altered brain areas in degree centrality (DC) and fractional amplitude of low-frequency fluctuation (fALFF) were identified between the two scans.

RESULTS

Eight patients completed the experiment. Stride length, maximum ankle height, maximum ankle horizontal displacement, gait speed, and range of shank motion significantly increased post-treatment (P <  0.05). fALFF in left middle frontal gyrus and DC in left cerebellum (corrected) increased, while fALFF in left inferior parietal lobule (corrected) during SA decreased, compared with those in tactile stimulation. A positive correlation was observed between right limb swings and both fALFF areas.

CONCLUSIONS

Differences in gait and brain analyses presented modulation to motor and brain activity in PD, thus, providing preliminary evidence for SA efficacy.

Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

Recruitment of frontal sensory circuits during visual discrimination

A long-range circuit linking the medial frontal cortex to the primary visual cortex (V1) has been proposed to mediate visual selective attention in mice during visually guided behavior. Here, we use in vivo two-photon functional imaging to measure the endogenous activity of axons of A24b/M2 neurons from this region projecting to layer 1 of V1 (A24b/M2-V1_axons) in mice either passively viewing stimuli or performing a go/no-go visually guided task. We observe that while A24b/M2-V1_axons are recruited under these conditions, this is not linked to enhancement of neural or behavioral measures of sensory coding. Instead, A24b/M2-V1_axon activity is associated with licking behavior, modulated by reward, and biased toward the sensory cortical hemisphere representing the stimulus currently being discriminated.

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Endogenous A24b/M2-V1 axon activity elevation does not enhance sensory processing

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A24b/M2-V1 axon activity is correlated with licking

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A24b/M2-V1 axon lick-correlated activity is modulated by reward

Stimulus-Selective Response Plasticity in Primary Visual Cortex: Progress and Puzzles

Stimulus-selective response plasticity (SRP) is a robust and lasting modification of primary visual cortex (V1) that occurs in response to exposure to novel visual stimuli. It is readily observed as a pronounced increase in the magnitude of visual evoked potentials (VEPs) recorded in response to phase-reversing grating stimuli in neocortical layer 4. The expression of SRP at the individual neuron level is equally robust, but the qualities vary depending on the neuronal type and how activity is measured. This form of plasticity is highly selective for stimulus features such as stimulus orientation, spatial frequency, and contrast. Several key insights into the significance and underlying mechanisms of SRP have recently been made. First, it occurs concomitantly and shares core mechanisms with behavioral habituation, indicating that SRP reflects the formation of long-term familiarity that can support recognition of innocuous stimuli. Second, SRP does not manifest within a recording session but only emerges after an off-line period of several hours that includes sleep. Third, SRP requires not only canonical molecular mechanisms of Hebbian synaptic plasticity within V1, but also the opposing engagement of two key subclasses of cortical inhibitory neuron: the parvalbumin- and somatostatin-expressing GABAergic interneurons. Fourth, pronounced shifts in the power of cortical oscillations from high frequency (gamma) to low frequency (alpha/beta) oscillations provide respective readouts of the engagement of these inhibitory neuronal subtypes following familiarization. In this article we will discuss the implications of these findings and the outstanding questions that remain to gain a deeper understanding of this striking form of experience-dependent plasticity.

Neocortex saves energy by reducing coding precision during food scarcity

Information processing is energetically expensive. In the mammalian brain, it is unclear how information coding and energy use are regulated during food scarcity. Using whole-cell recordings and two-photon imaging in layer 2/3 mouse visual cortex, we found that food restriction reduced AMPA receptor conductance, reducing synaptic ATP use by 29%. Neuronal excitability was nonetheless preserved by a compensatory increase in input resistance and a depolarized resting potential. Consequently, neurons spiked at similar rates as controls but spent less ATP on underlying excitatory currents. This energy-saving strategy had a cost because it amplified the variability of visually-evoked subthreshold responses, leading to a 32% broadening of orientation tuning and impaired fine visual discrimination. This reduction in coding precision was associated with reduced levels of the fat mass-regulated hormone leptin and was restored by exogenous leptin supplementation. Our findings reveal that metabolic state dynamically regulates the energy spent on coding precision in neocortex.

Homeostatic regulation through strengthening of neuronal network-correlated synaptic inputs

Homeostatic regulation is essential for stable neuronal function. Several synaptic mechanisms of homeostatic plasticity have been described, but the functional properties of synapses involved in homeostasis are unknown. We used longitudinal two-photon functional imaging of dendritic spine calcium signals in visual and retrosplenial cortices of awake adult mice to quantify the sensory deprivation-induced changes in the responses of functionally identified spines. We found that spines whose activity selectively correlated with intrinsic network activity underwent tumor necrosis factor alpha (TNF-α)-dependent homeostatic increases in their response amplitudes, but spines identified as responsive to sensory stimulation did not. We observed an increase in the global sensory-evoked responses following sensory deprivation, despite the fact that the identified sensory inputs did not strengthen. Instead, global sensory-evoked responses correlated with the strength of network-correlated inputs. Our results suggest that homeostatic regulation of global responses is mediated through changes to intrinsic network-correlated inputs rather than changes to identified sensory inputs thought to drive sensory processing.

Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships

One of the main goals of current systems neuroscience is to understand how neuronal populations integrate sensory information to inform behavior. However, estimating stimulus or behavioral information that is encoded in high-dimensional neuronal populations is challenging. We propose a method based on parametric copulas which allows modeling joint distributions of neuronal and behavioral variables characterized by different statistics and timescales. To account for temporal or spatial changes in dependencies between variables, we model varying copula parameters by means of Gaussian Processes (GP). We validate the resulting Copula-GP framework on synthetic data and on neuronal and behavioral recordings obtained in awake mice. We show that the use of a parametric description of the high-dimensional dependence structure in our method provides better accuracy in mutual information estimation in higher dimensions compared to other non-parametric methods. Moreover, by quantifying the redundancy between neuronal and behavioral variables, our model exposed the location of the reward zone in an unsupervised manner (i.e., without using any explicit cues about the task structure). These results demonstrate that the Copula-GP framework is particularly useful for the analysis of complex multidimensional relationships between neuronal, sensory and behavioral variables.

Understanding the relationship between a set of variables is a common problem in many fields, such as weather forecast or stock market data. In neuroscience, one of the main challenges is to characterize the dependencies between neuronal activity, sensory stimuli and behavioral outputs. A method of choice for modeling such statistical dependencies is based on copulas, which disentangle dependencies from single variable statistics. To account for changes in dependencies, we model changes in copula parameters by means of Gaussian Processes, conditioned on a task-related variable. The novelty of our approach includes 1) explicit modeling of the dependencies; and 2) combining different copulas to describe experimentally observed variability. We validate the goodness-of-fit as well as information estimates on synthetic data and on recordings from the visual cortex of mice performing a behavioral task. Our parametric model demonstrates significantly better performance in describing high dimensional dependencies compared to other commonly used techniques. We demonstrate that our model can estimate information and predict behaviorally-relevant parameters of the task without providing any explicit cues to the model. Our results indicate that our model is interpretable in the context of neuroscience applications, scalable to large datasets and suitable for accurate statistical modeling and information estimation.

Three Water Restriction Schedules Used in Rodent Behavioral Tasks Transiently Impair Growth and Differentially Evoke a Stress Hormone Response without Causing Dehydration

Water restriction is commonly used to motivate rodents to perform behavioral tasks; however, its effects on hydration and stress hormone levels are unknown. Here, we report daily body weight and bi-weekly packed red blood cell volume and corticosterone (CORT) in adult male rats across 80 days for three commonly used water restriction schedules. We also assessed renal adaptation to water restriction using postmortem histologic evaluation of renal medulla. A control group received ad libitum water. After one week of water restriction, rats on all restriction schedules resumed similar levels of growth relative to the control group. Normal hydration was observed, and water restriction did not drive renal adaptation. An intermittent restriction schedule was associated with an increase in CORT relative to the control group. However, intermittent restriction evokes a stress response which could affect behavioral and neurobiological results. Our results also suggest that stable motivation in behavioral tasks may only be achieved after one week of restriction.

Visual plasticity: Illuminating the role of the hippocampus in cortical sensory encoding

The primary visual cortex has the capacity to store stimulus-specific information locally. A new study reveals a direct role for the hippocampus in experience-dependent cortical plasticity when visual stimuli are presented in a predictable temporal order.

V1 as an egocentric cognitive map

We typically distinguish between V1 as an egocentric perceptual map and the hippocampus as an allocentric cognitive map. In this article, we argue that V1 also functions as a post-perceptual egocentric cognitive map. We argue that three well-documented functions of V1, namely (i) the estimation of distance, (ii) the estimation of size, and (iii) multisensory integration, are better understood as post-perceptual cognitive inferences. This argument has two important implications. First, we argue that V1 must function as the neural correlates of the visual perception/cognition distinction and suggest how this can be accommodated by V1's laminar structure. Second, we use this insight to propose a low-level account of visual consciousness in contrast to mid-level accounts (recurrent processing theory; integrated information theory) and higher-level accounts (higher-order thought; global workspace theory). Detection thresholds have been traditionally used to rule out such an approach, but we explain why it is a mistake to equate visibility (and therefore the presence/absence of visual experience) with detection thresholds.

Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex

During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains incompletely understood. We combine electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three navigation-relevant areas: primary visual cortex (V1), retrosplenial cortex (RSC), and medial entorhinal cortex (MEC). Compared with V1 and RSC, path integration influences position estimates more in MEC, and conflicts between path integration and landmarks trigger remapping more readily. Whereas MEC codes position prospectively, V1 codes position retrospectively, and RSC is intermediate between the two. Lowered visual contrast increases the influence of path integration on position estimates only in MEC. These properties are most pronounced in a population of MEC neurons, overlapping with grid cells, tuned to distance run in darkness. These results demonstrate the specialized role that path integration plays in MEC compared with other navigation-relevant cortical areas.

How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model

The mouse, as a model organism to study the brain, gives us unprecedented experimental access to the mammalian cerebral cortex. By determining the cortex's cellular composition, revealing the interaction between its different components, and systematically perturbing these components, we are obtaining mechanistic insight into some of the most basic properties of cortical function. In this review, we describe recent advances in our understanding of how circuits of cortical neurons implement computations, as revealed by the study of mouse primary visual cortex. Further, we discuss how studying the mouse has broadened our understanding of the range of computations performed by visual cortex. Finally, we address how future approaches will fulfill the promise of the mouse in elucidating fundamental operations of cortex.

Rethinking Single Neuron Electrical Compartmentalization: Dendritic Contributions to Network Computation In Vivo

Decades of experimental and theoretical work support a now well-established theory that active dendritic processing contributes to the computational power of individual neurons. This theory is based on the high degree of electrical compartmentalization observed in the dendrites of single neurons in ex vivo preparations. Compartmentalization allows dendrites to conduct semi-independent operations on their inputs before final integration and output at the axon, producing a "network-in-a-neuron." However, recent in vivo functional imaging experiments in mouse cortex have reported surprisingly little evidence for strong dendritic compartmentalization. In this review, we contextualize these new findings and discuss their impact on the future of the field. Specifically, we consider how highly coordinated, and thus less compartmentalized, activity in soma and dendrites can contribute to cortical computations including nonlinear mixed selectivity, prediction/expectation, multiplexing, and credit assignment.

Organization of feedback projections to mouse primary visual cortex

Top-down, context-dependent modulation of visual processing has been a topic of wide interest, including in mouse primary visual cortex (V1). However, the organization of feedback projections to V1 is relatively unknown. Here, we investigated inputs to mouse V1 by injecting retrograde tracers. We developed a software pipeline that maps labeled cell bodies to corresponding brain areas in the Allen Reference Atlas. We identified more than 24 brain areas that provide inputs to V1 and quantified the relative strength of their projections. We also assessed the organization of the projections, based on either the organization of cell bodies in the source area (topography) or the distribution of projections across V1 (bias). Projections from most higher visual and some nonvisual areas to V1 showed both topography and bias. Such organization of feedback projections to V1 suggests that parts of the visual field are differentially modulated by context, which can be ethologically relevant for a navigating animal.

Enhanced modulation of cell-type specific neuronal responses in mouse dorsal auditory field during locomotion

As we move through the environment we experience constantly changing sensory input that must be merged with our ongoing motor behaviors - creating dynamic interactions between our sensory and motor systems. Active behaviors such as locomotion generally increase the sensory-evoked neuronal activity in visual and somatosensory cortices, but evidence suggests that locomotion largely suppresses neuronal responses in the auditory cortex. However, whether this effect is ubiquitous across different anatomical regions of the auditory cortex is largely unknown. In mice, auditory association fields such as the dorsal auditory cortex (AuD), have been shown to have different physiological response properties, protein expression patterns, and cortical as well as subcortical connections, in comparison to primary auditory regions (A1) - suggesting there may be important functional differences. Here we examined locomotion-related modulation of neuronal activity in cortical layers ⅔ of AuD and A1 using two-photon Ca²⁺ imaging in head-fixed behaving mice that are able to freely run on a spherical treadmill. We determined the proportion of neurons in these two auditory regions that show enhanced and suppressed sensory-evoked responses during locomotion and quantified the depth of modulation. We found that A1 shows more suppression and AuD more enhanced responses during locomotion periods. We further revealed differences in the circuitry between these auditory regions and motor cortex, and found that AuD is more highly connected to motor cortical regions. Finally, we compared the cell-type specific locomotion-evoked modulation of responses in AuD and found that, while subpopulations of PV-expressing interneurons showed heterogeneous responses, the population in general was largely suppressed during locomotion, while excitatory population responses were generally enhanced in AuD. Therefore, neurons in primary and dorsal auditory fields have distinct response properties, with dorsal regions exhibiting enhanced activity in response to movement. This functional distinction may be important for auditory processing during navigation and acoustically guided behavior.

Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex

Sequential temporal ordering and patterning are key features of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. To explore how cortical neuronal activity underpins sequence discrimination, we developed a task in which mice distinguished between tactile "word" sequences constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Mice could respond to the earliest possible cues allowing discrimination, effectively solving the task as a "detection of change" problem, but enhanced their performance when responding later. Optogenetic inactivation showed that the somatosensory cortex was necessary for sequence discrimination. Two-photon imaging in layer 2/3 of the primary somatosensory "barrel" cortex (S1bf) revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal's action decision and the trial outcome (presence or absence of the predicted reward). Many neurons were activated preceding goal-directed licking, thus reflecting the animal's learned action in response to the target sequence; these neurons were found as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory response tuning: neurons responding to stimulus features were found in naive mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf, sequence learning results in neurons whose activity reflects the learned association between target sequence and licking rather than a refined representation of sensory features.

Spatial modulation of visual responses arises in cortex with active navigation

During navigation, the visual responses of neurons in mouse primary visual cortex (V1) are modulated by the animal's spatial position. Here we show that this spatial modulation is similarly present across multiple higher visual areas but negligible in the main thalamic pathway into V1. Similar to hippocampus, spatial modulation in visual cortex strengthens with experience and with active behavior. Active navigation in a familiar environment, therefore, enhances the spatial modulation of visual signals starting in the cortex.

Neuronal representations of reward-predicting cues and outcome history with movement in the frontal cortex

Transformation of sensory inputs to goal-directed actions requires estimation of sensory-cue values based on outcome history. We conduct wide-field and two-photon calcium imaging of the mouse neocortex during classical conditioning with two cues with different water-reward probabilities. Although licking movement dominates the area-averaged activity over the whole dorsal neocortex, the dorsomedial frontal cortex (dmFrC) affects other dorsal frontal cortical activities, and its inhibition extinguishes differences in anticipatory licking between the cues. Many dorsal frontal and medial prefrontal cortical neurons are task related. Subsets of these neurons are more excited by the low-reward-predicting cue or unrewarded outcomes than by the high-reward-predicting cue or rewarded outcomes, respectively. Task-related activities of these neurons and the others are counterbalanced, so that population activity appears dominated by licking. The reward-predicting cue and outcome history are most strongly represented in dmFrC. Our results suggest that dmFrC is crucial for initiating cortical processes to select or inhibit action.

Spatial navigation signals in rodent visual cortex

During navigation, animals integrate sensory information with body movements to guide actions. The impact of both navigational and movement-related signals on cortical visual information processing remains largely unknown. We review recent studies in awake rodents that have revealed navigation-related signals in the primary visual cortex (V1) including speed, distance travelled and head-orienting movements. Both cortical and subcortical inputs convey self-motion related information to V1 neurons: for example, top-down inputs from secondary motor and retrosplenial cortices convey information about head movements and spatial expectations. Within V1, subtypes of inhibitory neurons are critical for the integration of navigation-related and visual signals. We conclude with potential functional roles of navigation-related signals in V1 including gain control, motor error signals and predictive coding.

Spatial Information Encoding across Multiple Neocortical Regions Depends on an Intact Hippocampus

There has been considerable research showing populations of neurons encoding for different aspects of space in the brain. Recently, several studies using two-photon calcium imaging and virtual navigation have identified "spatially" modulated neurons in the posterior cortex. We enquire here whether the presence of such spatial representations may be a cortex-wide phenomenon and, if so, whether these representations can be organized in the absence of the hippocampus. To this end, we imaged the dorsal cortex of mice running on a treadmill populated with tactile cues. A high percentage (40-80%) of the detected neurons exhibited sparse, spatially localized activity, with activity fields uniformly localized over the track. The development of this location specificity was impaired by hippocampal damage. Thus, there is a substantial population of neurons distributed widely over the cortex that collectively form a continuous representation of the explored environment, and hippocampal outflow is necessary to organize this phenomenon.SIGNIFICANCE STATEMENT Increasing evidence points to the role of the neocortex in encoding spatial information. Whether this feature is linked to hippocampal functions is largely unknown. Here, we systematically surveyed multiple regions in the dorsal cortex of the same animal for the presence of signals encoding for spatial position. We described populations of cortical neurons expressing sequential patterns of activity localized in space in primary, secondary, and associational areas. Furthermore, we showed that the formation of these spatial representations was impacted by hippocampal lesion. Our results indicate that hippocampal inputs are necessary to maintain a precise cortical representation of space.

Mouse Visual Cortex Is Modulated by Distance Traveled and by Theta Oscillations

The visual responses of neurons in the primary visual cortex (V1) are influenced by the animal's position in the environment [1-5]. V1 responses encode positions that co-fluctuate with those encoded by place cells in hippocampal area CA1 [2, 5]. This correlation might reflect a common influence of non-visual spatial signals on both areas. Place cells in CA1, indeed, do not rely only on vision; their place preference depends on the physical distance traveled [6-11] and on the phase of the 6-9 Hz theta oscillation [12, 13]. Are V1 responses similarly influenced by these non-visual factors? We recorded V1 and CA1 neurons simultaneously while mice performed a spatial task in a virtual corridor by running on a wheel and licking at a reward location. By changing the gain that couples the wheel movement to the virtual environment, we found that ∼20% of V1 neurons were influenced by the physical distance traveled, as were ∼40% of CA1 place cells. Moreover, the firing rate of ∼24% of V1 neurons was modulated by the phase of theta oscillations recorded in CA1 and the response profiles of ∼7% of V1 neurons shifted spatially across the theta cycle, analogous to the phase precession observed in ∼37% of CA1 place cells. The influence of theta oscillations on V1 responses was more prominent in putative layer 6. These results reveal that, in a familiar environment, sensory processing in V1 is modulated by the key non-visual signals that influence spatial coding in the hippocampus.

Movement-Related Signals in Sensory Areas: Roles in Natural Behavior

Recent studies have demonstrated prominent and widespread movement-related signals in the brain of head-fixed mice, even in primary sensory areas. However, it is still unknown what role these signals play in sensory processing. Why are these sensory areas 'contaminated' by movement signals? During natural behavior, animals actively acquire sensory information as they move through the environment and use this information to guide ongoing actions. In this context, movement-related signals could allow sensory systems to predict self-induced sensory changes and extract additional information about the environment. In this review we summarize recent findings on the presence of movement-related signals in sensory areas and discuss how their study, in the context of natural freely moving behaviors, could advance models of sensory processing.

Two stream hypothesis of visual processing for navigation in mouse

Vision research has traditionally been studied in stationary subjects observing stimuli, and rarely during navigation. Recent research using virtual reality environments for mice has revealed that responses even in the primary visual cortex are modulated by spatial context - identical scenes presented in different positions of a room can elicit different responses. Here, we review these results and discuss how information from visual areas can reach navigational areas of the brain. Based on the observation that mouse higher visual areas cover different parts of the visual field, we propose that spatial signals are processed along two-streams based on visual field coverage. Specifically, this hypothesis suggests that landmark related signals are processed by areas biased to the central field, and self-motion related signals are processed by areas biased to the peripheral field.

Reward Association Enhances Stimulus-Specific Representations in Primary Visual Cortex

The potential for neuronal representations of external stimuli to be modified by previous experience is critical for efficient sensory processing and improved behavioral outcomes. To investigate how repeated exposure to a visual stimulus affects its representation in mouse primary visual cortex (V1), we performed two-photon calcium imaging of layer 2/3 neurons and assessed responses before, during, and after the presentation of a repetitive stimulus over 5 consecutive days. We found a stimulus-specific enhancement of the neuronal representation of the repetitively presented stimulus when it was associated with a reward. This was observed both after mice actively learned a rewarded task and when the reward was randomly received. Stimulus-specific enhanced representation resulted both from neurons gaining selectivity and from increased response reliability in previously selective neurons. In the absence of reward, there was either no change in stimulus representation or a decreased representation when the stimulus was viewed at a fixed temporal frequency. Pairing a second stimulus with a reward led to a similar enhanced representation and increased discriminability between the equally rewarded stimuli. Single-neuron responses showed that separate subpopulations discriminated between the two rewarded stimuli depending on whether the stimuli were displayed in a virtual environment or viewed on a single screen. We suggest that reward-associated responses enable the generalization of enhanced stimulus representation across these V1 subpopulations. We propose that this dynamic regulation of visual processing based on the behavioral relevance of sensory input ultimately enhances and stabilizes the representation of task-relevant features while suppressing responses to non-relevant stimuli.

Reward Timing and Its Expression by Inhibitory Interneurons in the Mouse Primary Visual Cortex

The primary sensory cortex has historically been studied as a low-level feature detector, but has more recently been implicated in many higher-level cognitive functions. For instance, after an animal learns that a light predicts water at a fixed delay, neurons in the primary visual cortex (V1) can produce "reward timing activity" (i.e., spike modulation of various forms that relate the interval between the visual stimulus and expected reward). Local manipulations to V1 implicate it as a site of learning reward timing activity (as opposed to simply reporting timing information from another region via feedback input). However, the manner by which V1 then produces these representations is unknown. Here, we combine behavior, in vivo electrophysiology, and optogenetics to investigate the characteristics of and circuit mechanisms underlying V1 reward timing in the head-fixed mouse. We find that reward timing activity is present in mouse V1, that inhibitory interneurons participate in reward timing, and that these representations are consistent with a theorized network architecture. Together, these results deepen our understanding of V1 reward timing and the manner by which it is produced.

Representation of visual landmarks in retrosplenial cortex

The process by which visual information is incorporated into the brain’s spatial framework to represent landmarks is poorly understood. Studies in humans and rodents suggest that retrosplenial cortex (RSC) plays a key role in these computations. We developed an RSC-dependent behavioral task in which head-fixed mice learned the spatial relationship between visual landmark cues and hidden reward locations. Two-photon imaging revealed that these cues served as dominant reference points for most task-active neurons and anchored the spatial code in RSC. This encoding was more robust after task acquisition. Decoupling the virtual environment from mouse behavior degraded spatial representations and provided evidence that supralinear integration of visual and motor inputs contributes to landmark encoding. V1 axons recorded in RSC were less modulated by task engagement but showed surprisingly similar spatial tuning. Our data indicate that landmark representations in RSC are the result of local integration of visual, motor, and spatial information.

When moving through a city, people often use notable or familiar landmarks to help them navigate. Landmarks provide us with information about where we are and where we need to go next. But despite the ease with which we – and most other animals – use landmarks to find our way around, it remains unclear exactly how the brain makes this possible.

One area that seems to have a key role is the retrosplenial cortex, which is located deep within the back of the brain in humans. This area becomes more active when animals use visual landmarks to navigate. It is also one of the first brain regions to be affected in Alzheimer's disease, which may help to explain why patients with this condition can become lost and disoriented, even in places they have been many times before.

To find out how the retrosplenial cortex supports navigation, Fischer et al. measured its activity in mice exploring a virtual reality world. The mice ran through simulated corridors in which visual landmarks indicated where hidden rewards could be found. The activity of most neurons in the retrosplenial cortex was most strongly influenced by the mouse’s position relative to the landmark; for example, some neurons were always active 10 centimeters after the landmark.

In other experiments, when the landmarks were present but no longer indicated the location of a reward, the same neurons were much less active. Fischer et al. also measured the activity of the neurons when the mice were running with nothing shown on the virtual reality, and when they saw a landmark but did not run. Notably, the activity seen when the mice were using the landmarks to find rewards was greater than the sum of that recorded when the mice were just running or just seeing the landmark without a reward, making the “landmark response” an example of so-called supralinear processing.

Fischer et al. showed that visual centers of the brain send information about landmarks to retrosplenial cortex. But only the latter adjusts its activity depending on whether the mouse is using that landmark to navigate. These findings provide the first evidence for a “landmark code” at the level of neurons and lay the foundations for studying impaired navigation in patients with Alzheimer's disease. By showing that retrosplenial cortex neurons combine different types of input in a supralinear fashion, the results also point to general principles for how neurons in the brain perform complex calculations.

High and asymmetric somato-dendritic coupling of V1 layer 5 neurons independent of visual stimulation and locomotion

Active dendrites impact sensory processing and behaviour. However, it remains unclear how active dendritic integration relates to somatic output in vivo. We imaged semi-simultaneously GCaMP6s signals in the soma, trunk and distal tuft dendrites of layer 5 pyramidal neurons in the awake mouse primary visual cortex. We found that apical tuft signals were dominated by widespread, highly correlated calcium transients throughout the tuft. While these signals were highly coupled to trunk and somatic transients, the frequency of calcium transients was found to decrease in a distance-dependent manner from soma to tuft. Ex vivo recordings suggest that low-frequency back-propagating action potentials underlie the distance-dependent loss of signals, while coupled somato-dendritic signals can be triggered by high-frequency somatic bursts or strong apical tuft depolarization. Visual stimulation and locomotion increased neuronal activity without affecting somato-dendritic coupling. High, asymmetric somato-dendritic coupling is therefore a widespread feature of layer 5 neurons activity in vivo.